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James' debugging of the EP/GP interface

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It seems that the GP-EP algorithm works now.
This commit is contained in:
Ricardo Andrade 2013-02-01 13:45:55 +00:00
parent eb04cbed63
commit f941d629e6
3 changed files with 11 additions and 8 deletions
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11
GPy/likelihoods/EP.py
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@ -27,7 +27,7 @@ class EP(likelihood):
#initial values for the GP variables
self.Y = np.zeros((self.N,1))
self.variance = np.zeros((self.N,self.N))#np.eye(self.N)
self.covariance_matrix = np.eye(self.N)
self.Z = 0
self.YYT = None
@ -50,8 +50,9 @@ class EP(likelihood):
mu_diff_2 = (self.v_/self.tau_ - mu_tilde)**2
self.Z = np.sum(np.log(self.Z_hat)) + 0.5*np.sum(np.log(sigma_sum)) + 0.5*np.sum(mu_diff_2/sigma_sum) #Normalization constant, aka Z_ep
self.Y = mu_tilde[:,None]
self.precsion = self.tau_tilde[:,None]
self.Y = mu_tilde[:,None]
self.YYT = np.dot(self.Y,self.Y.T)
self.precision = self.tau_tilde
self.covariance_matrix = np.diag(1./self.precision)
def fit_full(self,K):
@ -61,9 +62,11 @@ class EP(likelihood):
"""
#Prior distribution parameters: p(f|X) = N(f|0,K)
self.tau_tilde = np.zeros(self.N)
self.v_tilde = np.zeros(self.N)
#Initial values - Posterior distribution parameters: q(f|X,Y) = N(f|mu,Sigma)
self.mu = np.zeros(self.N)
self.Sigma = K.copy() - self.variance.copy()
self.Sigma = K.copy()
"""
Initial values - Cavity distribution parameters: